Online identification methods for a class of Hammerstein nonlinear systems using the adaptive particle filtering

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 186, P. 115181 - 115181

Published: July 1, 2024

Language: Английский

---

Auxiliary model maximum likelihood gradient‐based iterative identification for feedback nonlinear systems

Optimal Control Applications and Methods, Journal Year: 2024, Volume and Issue: 45(5), P. 2346 - 2363

Published: June 17, 2024

Abstract This article considers the iterative identification problems for a class of feedback nonlinear systems with moving average noise. The model contains both dynamic linear module and static module, which brings challenges to identification. By utilizing key term separation technique, unknown parameters from modules are included in parameter vector. Furthermore, an auxiliary maximum likelihood gradient‐based algorithm is derived estimate parameters. In addition, stochastic gradient as comparison. numerical simulation results indicate that can effectively get more accurate estimates than algorithm.

Language: Английский

---

The filtering-based recursive least squares identification and convergence analysis for nonlinear feedback control systems with coloured noises

International Journal of Systems Science, Journal Year: 2024, Volume and Issue: 55(16), P. 3461 - 3484

Published: July 7, 2024

Language: Английский

---

Auxiliary Model‐Based Maximum Likelihood Multi‐Innovation Forgetting Gradient Identification for a Class of Multivariable Systems

Optimal Control Applications and Methods, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

ABSTRACT Through dividing a multivariable system into several subsystems, this paper derives the sub‐identification model. Utilizing obtained model, an auxiliary model‐based maximum likelihood forgetting gradient algorithm is derived. Considering enhancing parameter estimation accuracy, multi‐innovation (AM‐ML‐MIFG) proposed taking advantage of identification theory. Simulation results test effectiveness algorithms, and confirm that AM‐ML‐MIFG has satisfactory performance in capturing dynamic properties system.

Language: Английский

---

Highly efficient three-stage maximum likelihood recursive least squares identification method for multiple-input multiple-output systems

Systems & Control Letters, Journal Year: 2025, Volume and Issue: 200, P. 106094 - 106094

Published: April 6, 2025

Language: Английский

---

Iterative parameter identification for Hammerstein systems with ARMA noises by using the filtering identification idea

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: 38(9), P. 3134 - 3160

Published: June 28, 2024

Summary In practical applications, many processes have nonlinear characteristics that require models for accurate description. However, constructing such and determining their parameters are a challenging task. This article explores filtered identification methods estimating the of particular type Hammerstein systems with ARMA noise. An auxiliary model‐based least squares algorithm is developed based on model idea. A hierarchical utilizes principle proposed to enhance computational efficiency. Additionally, key term separation technique employed express system output as linear combination parameters, allowing be decomposed into smaller subsystems more efficient estimation parameters. Simulation results demonstrate effectiveness these algorithms.

Language: Английский

---

Parameter Estimation and Model-free Multi-innovation Adaptive Control Algorithms

International Journal of Control Automation and Systems, Journal Year: 2024, Volume and Issue: 22(11), P. 3509 - 3524

Published: Nov. 1, 2024

Language: Английский

---

Parameter estimation methods for time‐invariant continuous‐time systems from dynamical discrete output responses based on the Laplace transforms

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: 38(9), P. 3213 - 3232

Published: July 3, 2024

Summary In industrial process control systems, parameter estimation is crucial for controller design and model analysis. This article examines the issue of identifying parameters in continuous‐time models. presents a stochastic gradient algorithm recursive least squares continuous systems. It derives identification linear systems based on Laplace transforms input output To prove that techniques given here work, we have included simulated example.

Language: Английский

---

Sliding Window Iterative Identification for Nonlinear Closed‐Loop Systems Based on the Maximum Likelihood Principle

International Journal of Robust and Nonlinear Control, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

ABSTRACT The parameter estimation problem for the nonlinear closed‐loop systems with moving average noise is considered in this article. For purpose of overcoming difficulty that dynamic linear module and static lead to identification complexity issues, unknown parameters from both modules are included a vector by use key term separation technique. Furthermore, an sliding window maximum likelihood least squares iterative algorithm gradient derived estimate parameters. numerical simulation indicates efficiency proposed algorithms.

Language: Английский

---

Auxiliary model‐based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output‐error systems

International Journal of Adaptive Control and Signal Processing, Journal Year: 2024, Volume and Issue: 38(10), P. 3268 - 3289

Published: July 29, 2024

Summary This paper deals with the problem of parameter estimation for feedback nonlinear output‐error systems. The auxiliary model‐based recursive least squares algorithm and stochastic gradient are derived estimation. Based on process theory, convergence proposed algorithms proved. simulation results indicate that can estimate parameters systems effectively.

Language: Английский

---